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Connect4 AI: Deep Q-Learning

Website

https://connect4-ai-liard.vercel.app/

Preview

Preview of project UI

Description

This is a 3D web application enabling users to play Connect4 against an AI trained through self-play using deep Q-learning. The AI, having learned strategies from over 300,000 self-play games, is served through a backend system utilizing TensorFlow, which the web app consults to determine the AI's moves.

Running the Project Locally

1. Prerequisites

  • Python3 (3.11.7)
  • pip3 (23.2.1)
  • Node.js (v20.10.0)
  • npm (10.3.0)

2. Clone the Repository

git clone https://github.com/fjcollyer/Connect4-AI-Deep-Q-Learning.git &&
cd Connect4-AI-Deep-Q-Learning

3. Run the API that serves the Deep Q-Learning Model

cd api &&
pip3 install -r requirements.txt &&
python3 app.py

4. Run the UI

cd ui &&
npm i &&
npm run dev

5. Note

By default the API will run on http://127.0.0.1:8080. If it runs on a different port you must update the code in "ui/src/Game.js" to use the correct URL.

Training the model

1. Set hyperparameters and parameters

In "api/train/main.py" there is a Config class that contains most of the important hyperparameters and parameters.

2. Run the training script

cd api &&
python3 -m train.main

This will generate "api/agents/" where the model is periodically saved to.

3. Run final evaluation of the model

python3 -m train.sim_vs_random

This plays the saved models from 'api/agents/' against an opponent that makes random moves. When done it will plot the performance of the different iterations of the model and save it to '2024learning_progression.png'.